Sentiment Analysis at SEPLN (TASS)-2019: Sentiment Analysis at Tweet level using Deep Learning
This work addresses sentiment classification for informal Spanish tweets in various dialects, but it is incremental as it applies existing deep learning methods to a new dataset.
The paper tackled sentiment analysis of Spanish tweets across multiple dialects by developing a system using LSTM networks, achieving classification into four sentiment classes for the TASS-2019 shared task.
This paper describes the system submitted to "Sentiment Analysis at SEPLN (TASS)-2019" shared task. The task includes sentiment analysis of Spanish tweets, where the tweets are in different dialects spoken in Spain, Peru, Costa Rica, Uruguay and Mexico. The tweets are short (up to 240 characters) and the language is informal, i.e., it contains misspellings, emojis, onomatopeias etc. Sentiment analysis includes classification of the tweets into 4 classes, viz., Positive, Negative, Neutral and None. For preparing the proposed system, we use Deep Learning networks like LSTMs.